Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model
AbstractLung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet2...
Source: Journal of Digital Imaging - March 11, 2024 Category: Radiology Source Type: research
CT-Based Evaluation of the Shape of the Diaphragm Using 3D Slicer
In conclusion,3D Slicer can be applied to CT scans for determining the shape of the diaphragm in COPD patients. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - March 11, 2024 Category: Radiology Source Type: research
Feature Fusion for Multi-Coil Compressed MR Image Reconstruction
AbstractMagnetic resonance imaging (MRI) occupies a pivotal position within contemporary diagnostic imaging modalities, offering non-invasive and radiation-free scanning. Despite its significance, MRI ’s principal limitation is the protracted data acquisition time, which hampers broader practical application. Promising deep learning (DL) methods for undersampled magnetic resonance (MR) image reconstruction outperform the traditional approaches in terms of speed and image quality. However, the i ntricate inter-coil correlations have been insufficiently addressed, leading to an underexploitation of the rich information inh...
Source: Journal of Digital Imaging - March 8, 2024 Category: Radiology Source Type: research
Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images
In this study, we investigated the feasibility of reducing the number of labeled images in a limited set of unlabeled medical images. The unlabeled chest X-ray (CXR) images were pretrained using the SimCLR framework, and then the representations were fine-tuned as supervised learning for the target task. A total of 2000 task-specific CXR images were used to perform binary classification of coronavirus disease 2019 (COVID-19) and normal cases. The results demonstrate that the performance of pretraining on task-specific unlabeled CXR images can be maintained when the number of labeled CXR images is reduced by approximately 4...
Source: Journal of Digital Imaging - March 8, 2024 Category: Radiology Source Type: research
Identification and Localization of Indolent and Aggressive Prostate Cancers Using Multilevel Bi-LSTM
AbstractIdentifying indolent and aggressive prostate cancers is a critical problem for optimal treatment. The existing approaches of prostate cancer detection are facing challenges as the techniques rely on ground truth labels with limited accuracy, and histological similarity, and do not consider the disease pathology characteristics, and indefinite differences in appearance between the cancerous and healthy tissue lead to many false positive and false negative interpretations. Hence, this research introduces a comprehensive framework designed to achieve accurate identification and localization of prostate cancers, irresp...
Source: Journal of Digital Imaging - March 6, 2024 Category: Radiology Source Type: research
Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays
AbstractWe aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both m...
Source: Journal of Digital Imaging - March 6, 2024 Category: Radiology Source Type: research
Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm
In conclusion, intravenous infusion of iodine contrast agent transiently decreased the tracheal diameter and both lung volumes. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - March 6, 2024 Category: Radiology Source Type: research
Independently Trained Multi-Scale Registration Network Based on Image Pyramid
AbstractImage registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of di...
Source: Journal of Digital Imaging - March 5, 2024 Category: Radiology Source Type: research
DilatedToothSegNet: Tooth Segmentation Network on 3D Dental Meshes Through Increasing Receptive Vision
AbstractThe utilization of advanced intraoral scanners to acquire 3D dental models has gained significant popularity in the fields of dentistry and orthodontics. Accurate segmentation and labeling of teeth on digitized 3D dental surface models are crucial for computer-aided treatment planning. At the same time, manual labeling of these models is a time-consuming task. Recent advances in geometric deep learning have demonstrated remarkable efficiency in surface segmentation when applied to raw 3D models. However, segmentation of the dental surface remains challenging due to the atypical and diverse appearance of the patient...
Source: Journal of Digital Imaging - March 5, 2024 Category: Radiology Source Type: research
Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion
This study aimed to develop a model based on radiomics and deep learning features to predict the ablation rate in patients with adenomyosis undergoing high-intensity focused ultrasound (HIFU) therapy. A total of 119 patients with adenomyosis who received HIFU therapy were retrospectively analyzed. Participants were included in the training and testing queues in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI) images, and VGG-19 was used to extract advanced deep features. An ensemble model based on multi-model fusion for predicting the efficacy of HIFU in adenomyosis was proposed, which consist...
Source: Journal of Digital Imaging - March 5, 2024 Category: Radiology Source Type: research
Computerized Segmentation Method for Nonmasses on Breast DCE-MRI Images Using ResUNet++ with Slice Sequence Learning and Cross-Phase Convolution
AbstractThe purpose of this study was to develop a computerized segmentation method for nonmasses using ResUNet++ with a slice sequence learning and cross-phase convolution to analyze temporal information in breast dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images. The dataset consisted of a series of DCE-MRI examinations from 54 patients, each containing three-phase images, whic h included one image that was acquired before contrast injection and two images that were acquired after contrast injection. In the proposed method, the region of interest (ROI) slice images are first extracted from...
Source: Journal of Digital Imaging - March 5, 2024 Category: Radiology Source Type: research
Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm
In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts. 124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA) and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and wa s correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman’s...
Source: Journal of Digital Imaging - March 4, 2024 Category: Radiology Source Type: research
Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis
This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by scre ening images and highlighting regions of interest, thus improving workflow. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - March 4, 2024 Category: Radiology Source Type: research
Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique
AbstractImage segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in sem...
Source: Journal of Digital Imaging - March 4, 2024 Category: Radiology Source Type: research
From CNN to Transformer: A Review of Medical Image Segmentation Models
AbstractMedical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Moreover, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. Recently, the Segment Anything Model (SAM) and its variants have also been attempted for medical ...
Source: Journal of Digital Imaging - March 4, 2024 Category: Radiology Source Type: research
